当前位置 博文首页 > Rango_lhl：FlinkSQL写入Kafka/ES/MySQL示例-JAVA
Flink的API做了4层的封装，上两层TableAPI、SQL语法相对简单便于编写，面对小需求可以快速上手解决，本文参考官网及部分线上教程编写source端、sink端代码，分别读取socket、kafka及文本作为source，并将流数据输出写入Kafka、ES及MySQL，方便后续查看使用。
说明：这里使用connect及DDL两种写法，connect满足Flink1.10及以前版本使用，目前官方文档均是以DDL写法作为介绍，建议1.10以后的版本使用DDL写法操作，通用性更强。
1.1 基础环境建立，方便演示并行度为1且不设置CK
//建立Stream环境，设置并行度为1
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment().setParallelism(1);
//建立Table环境
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
1.2 读取Socket端口数据，并使用TableAPI及SQL两种方式查询
//读取服务器9999端口数据，并转换为对应JavaBean
SingleOutputStreamOperator<WaterSensor> mapDS = env.socketTextStream("hadoop102", 9999)
.map(value -> {
String[] split = value.split(",");
return new WaterSensor(split[0]
, Long.parseLong(split[1])
, Integer.parseInt(split[2]));});
//创建表：将流转换成动态表。
Table table = tableEnv.fromDataStream(mapDS);
//对动态表进行查询,TableAPI方式
Table selectResult = table.where($("id").isEqual("ws_001")).select($("id"), $("ts"), $("vc"));
//对动态表镜像查询，SQL方式-未注册表
Table selectResult = tableEnv.sqlQuery("select * from " + table);
1.3 读取文本（FileSystem）数据，并使用TableAPI进行查询
//Flink1.10写法使用connect方式，读取txt文件并建立临时表
tableEnv.connect(new FileSystem().path("input/sensor.txt"))
.withFormat(new Csv().fieldDelimiter(',').lineDelimiter("\n"))
.withSchema(new Schema().field("id", DataTypes.STRING())
.field("ts", DataTypes.BIGINT())
.field("vc",DataTypes.INT()))
.createTemporaryTable("sensor");
//转换成表对象，对表进行查询。SQL写法参考Socket段写法
Table table = tableEnv.from("sensor");
Table selectResult = table.groupBy($("id")).aggregate($("id").count().as("id_count"))select($("id"), $("id_count"));
1.4 消费Kafka数据，并使用TableAPI进行查询，分别用conncet及DDL写法
//Flink1.10写法使用connect方式，消费kafka对应主题并建立临时表
tableEnv.connect(new Kafka().version("universal")
.topic("sensor")
.startFromLatest()
.property(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092")
.property(ConsumerConfig.GROUP_ID_CONFIG,"BD"))//消费者组
.withSchema(new Schema().field("id", DataTypes.STRING())
.field("ts", DataTypes.BIGINT())
.field("vc",DataTypes.INT()))
.withFormat(new Csv())
.createTemporaryTable("sensor");
//Flink1.10以后使用DDL写法
tableEnv.executeSql("CREATE TABLE sensor (" +
" `id` STRING," +
" `ts` BIGINT," +
" `vc` INT" +
") WITH (" +
" 'connector' = 'kafka'," +
" 'topic' = 'sensor'," +
" 'properties.bootstrap.servers' = 'hadoop102:9092'," +
" 'properties.group.id' = 'BD'," +
" 'scan.startup.mode' = 'latest-offset'," +
" 'format' = 'csv'" +
")");
//转换成表对象，对表进行查询。SQL写法参考Socket段写法
Table table = tableEnv.from("sensor");
Table selectResult = table.groupBy($("id")).aggregate($("id").count().as("id_count"))
.select($("id"), $("id_count"));
2.1 写入文本文件
//创建表：创建输出表，connect写法
tableEnv.connect(new FileSystem().path("out/sensor.txt"))
.withFormat(new Csv())
.withSchema(new Schema().field("id", DataTypes.STRING())
.field("ts", DataTypes.BIGINT())
.field("vc",DataTypes.INT()))
.createTemporaryTable("sensor");
//将数据写入到输出表中即实现sink写入，selectResult则是上面source侧查询出来的结果表
selectResult.executeInsert("sensor");
2.2 写入Kafka
//connect写法
tableEnv.connect(new Kafka().version("universal")
.topic("sensor")
.sinkPartitionerRoundRobin() //轮询写入
.property(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop102:9092"))
.withSchema(new Schema().field("id", DataTypes.STRING())
.field("ts", DataTypes.BIGINT())
.field("vc",DataTypes.INT()))
.withFormat(new Json())
.createTemporaryTable("sensor");
//DDL写法
tableEnv.executeSql("CREATE TABLE sensor (" +
" `id` STRING," +
" `ts` BIGINT," +
" `vc` INT" +
") WITH (" +
" 'connector' = 'kafka'," +
" 'topic' = 'sensor'," +
" 'properties.bootstrap.servers' = 'hadoop102:9092'," +
" 'format' = 'json'" +
")");
//将数据写入到输出表中即实现sink写入，selectResult则是上面source侧查询出来的结果表
selectResult.executeInsert("sensor");
2.3 写入MySQL（JDBC方式，这里手动导入了mysql-connector-java-5.1.9.jar）
//DDL
tableEnv.executeSql("CREATE TABLE sink_sensor (" +
" id STRING," +
" ts BIGINT," +
" vc INT," +
" PRIMARY KEY (id) NOT ENFORCED" +
") WITH (" +
" 'connector' = 'jdbc'," +
" 'url' = 'jdbc:mysql://hadoop102:3306/test?useSSL=false'," +
" 'table-name' = 'sink_test'," +
" 'username' = 'root'," +
" 'password' = '123456'" +
")");
//将数据写入到输出表中即实现sink写入，selectResult则是上面source侧查询出来的结果表
selectResult.executeInsert("sensor");
2.4 写入ES
//connect写法
tableEnv.connect(new Elasticsearch()
.index("sensor")
.documentType("_doc")
.version("7")
.host("localhost",9200,"http")
//设置为1，每行数据都写入是方便客户端输出展示，生产勿使用
.bulkFlushMaxActions(1))
.withSchema(new Schema()
.field("id", DataTypes.STRING())
.field("ts", DataTypes.BIGINT())
.field("vc",DataTypes.INT()))
.withFormat(new Json())
.inAppendMode()
.createTemporaryTable("sensor");
//DDL写法
tableEnv.executeSql("CREATE TABLE sensor (" +
" id STRING," +
" ts BIGINT," +
" vc INT," +
" PRIMARY KEY (id) NOT ENFORCED" +
") WITH (" +
" 'connector' = 'elasticsearch-7'," +
" 'hosts' = 'http://localhost:9200'," +
" 'index' = 'users'," +
" 'sink.bulk-flush.max-actions' = '1')";)
//将数据写入到输出表中即实现sink写入，selectResult则是上面source侧查询出来的结果表
selectResult.executeInsert("sensor");
依赖部分pom.xml
<properties>
<java.version>1.8</java.version>
<maven.compiler.source>${java.version}</maven.compiler.source>
<maven.compiler.target>${java.version}</maven.compiler.target>
<flink.version>1.12.0</flink.version>
<scala.version>2.12</scala.version>
<hadoop.version>3.1.3</hadoop.version>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_${scala.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch</artifactId>
<version>7.8.0</version>
</dependency>
<!-- elasticsearch 的客户端 -->
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.8.0</version>
</dependency>
<!-- elasticsearch 依赖 2.x 的 log4j -->
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-api</artifactId>
<version>2.8.2</version>
</dependency>
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-core</artifactId>
<version>2.8.2</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>2.9.9</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-elasticsearch7_${scala.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.16</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-api-java-bridge_${scala.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner-blink_${scala.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-csv</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka_${scala.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-json</artifactId>
<version>${flink.version}</version>
</dependency>
</dependencies>
</project>
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